GexMolGen:通过大型语言模型编码基因表达签名的跨模式生成类似突击的分子
Jiabei Cheng1, Xiaoyong Pan1, Yi Fang1
1Department of Automation, School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, 800 Dongchuan Road, Minhang District, Shanghai 200240, China.
Briefings in bioinformatics
|October 29, 2024
概括
GexMolGen从基因表达数据直接生成新型药物分子,绕过传统的基因标探索. 这种创新方法使药物发现的有效跨模式查成为可能.
科学领域:
- 计算化学是一种计算化学.
- 生物信息学是一种生物信息学.
- 药物发现 药物发现
背景情况:
- 设计具有特定生物活性的新分子对于药物发现至关重要.
- 传统方法依赖于选现有实验结果,这限制了数据范围和跨模式比较.
- 基因表达特征为分子生成提供了一个潜在的替代方案.
研究的目的:
- 开发一种新的计算模型,仅使用基因表达特征来生成类似击中分子.
- 通过将基因表达数据与分子结构联系起来,实现直接的交叉模式查.
- 为了克服传统分子选方法的局限性.
主要方法:
- 提出了使用"先对齐然后生成"策略的GexMolGen (基于基因表达的分子生成器).
- 在共享的映射空间中对齐基因表达特征和分子,用于跨模态过渡.
- 为了输入灵活性,使用单细胞大语言模型和基于支架的分子模型来确定有效性.
- 从转换的分子嵌入中生成分子图.
主要成果:
- 使用域内和域外的转录组数据,GexMolGen成功生成了与已知参考非常相似的分子.
- 该模型在产生有效的分子结构方面表现出有效性.
- 经验结果验证了该模型的跨模式查能力.
结论:
- GexMolGen为新分子设计和药物发现提供了一个强大的新范式.
- 该模型有效地从基因表达数据中生成生物相关的分子.
- GexMolGen作为交叉模式查的可靠工具,推进药物发现管道.
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